Fetching the paper…
Reading the bibliography…
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions.
Statistical theory of extreme values and some practical applications: a series of lectures . Vol. 33
Emil Julius Gumbel. 1948 · 1948
Earlier work this paper cites.
Recommender systems
Paul Resnick and Hal R Varian. 1997 · 1997
Earlier work this paper cites.
Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence . Morgan Kaufmann Publishers Inc., 43–52
John S Breese, David Heckerman, and Carl Kadie. 1998 · 1998
Earlier work this paper cites.
Content-based book recommending using learning for text categorization. In Proceedings of the fifth ACM conference on Digital libraries . ACM, 195–204
Raymond J Mooney and Loriene Roy. 2000 · 2000
Earlier work this paper cites.
Amazon. com recommendations: Item-to-item collaborative filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
A survey of web information extraction systems
Chia-Hui Chang, Mohammed Kayed, Moheb R Girgis, and Khaled F Shaalan. 2006 · 2006
Earlier work this paper cites.
Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Conference on . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011 · 2011
Earlier work this paper cites.
Recommendations in location-based social networks: a survey
Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015 · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In International Conference on Machine Learning . 448–456
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web . 111–112
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . ACM, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 1149–1154
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016 · 2016
Earlier work this paper cites.
Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . 17–22
Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016 · 2016
Earlier work this paper cites.
Personal recommendation using deep recurrent neural networks in NetEase. In Data Engineering (ICDE), 2016 IEEE 32nd International Conference on . IEEE, 1218–1229
Sai Wu, Weichao Ren, Chengchao Yu, Gang Chen, Dongxiang Zhang, and Jingbo Zhu. 2016 · 2016
Cited alongside, same era.
Deep learning over multi-field categorical data. In European conference on information retrieval . Springer, 45–57
Weinan Zhang, Tianming Du, and Jun Wang. 2016 · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le. 2016 · 2016
Cited alongside, same era.
Smash: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston. 2017 · 2017
Cited alongside, same era.
SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin. 2018 · 2018
Later among the works it cites.
Practical block-wise neural network architecture generation. In Proceedings of the IEEE conference on computer vision and pattern recognition
Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu. 2018 · 2018
Later among the works it cites.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Later among the works it cites.
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence . 1725–1731
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 355–364
Xiangnan He and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Personalized Deep Learning for Tag Recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer
Hanh TH Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rego Drumond, and Lars Schmidt-Thieme. 2017 · 2017
Cited alongside, same era.
Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Deep Learning based Recommender System: A Survey and New Perspectives
Shuai Zhang, Lina Yao, and Aixin Sun. 2017 · 2017
Cited alongside, same era.
Path-level network transformation for efficient architecture search
Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, and Yong Yu. 2018 · 2018
Cited alongside, same era.
xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018 · 2018
Cited alongside, same era.
Ting Chen, Lala Li, and Yizhou Sun. 2019 · 2019
Later among the works it cites.
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, and James Zou. 2019 · 2019
Later among the works it cites.
Neural input search for large scale recommendation models
Manas R Joglekar, Cong Li, Jay K Adams, Pranav Khaitan, and Quoc V Le. 2019 · 2019
Later among the works it cites.
Regularized Evolution for Image Classifier Architecture Search. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 4780–4789
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le. 2019 · 2019
Later among the works it cites.
Autoint: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 1161–1170
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019 · 2019
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2820–2828
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le. 2019 · 2019
Later among the works it cites.
Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 · 2019
Later among the works it cites.
Differentiable Neural Input Search for Recommender Systems
Weiyu Cheng, Yanyan Shen, and Linpeng Huang. 2020 · 2020
Closest in time.
Neural input search for large scale recommendation models. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2387–2397
Manas R Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K Adams, Pranav Khaitan, Jiahui Liu, and Quoc V Le. 2020 · 2020
Closest in time.
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, and Ed H Chi. 2020 · 2020
Closest in time.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Jiliang Tang. 2020 · 2020
Closest in time.
FuxiCTR: An Open Benchmark for Click-Through Rate Prediction
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2020 · 2020
Closest in time.